Bitcoin Candlestick Chart Over Past 30 Days using Coingecko API¶

This project leverages the CoinGecko API and the power of Python to create an informative and visually appealing Bitcoin Candlestick Chart spanning the last 30 days. A Candlestick Chart is a widely used financial charting tool that provides valuable insights into the price movements of cryptocurrencies, including Bitcoin.

Install and importing Library¶

In [1]:
!pip install pycoingecko
from pycoingecko import CoinGeckoAPI
import pandas as pd

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: pycoingecko in c:\users\dinor\appdata\roaming\python\python311\site-packages (3.1.0)
Requirement already satisfied: requests in c:\programdata\anaconda3\lib\site-packages (from pycoingecko) (2.31.0)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\programdata\anaconda3\lib\site-packages (from requests->pycoingecko) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in c:\programdata\anaconda3\lib\site-packages (from requests->pycoingecko) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in c:\programdata\anaconda3\lib\site-packages (from requests->pycoingecko) (1.26.16)
Requirement already satisfied: certifi>=2017.4.17 in c:\programdata\anaconda3\lib\site-packages (from requests->pycoingecko) (2023.7.22)

Helper Funcitons¶

In [2]:
from datetime import datetime
import time

def unix_time(year,month,day,hour,second):
    date_time = datetime.datetime(year,month,day,hour,second)
    return time.mktime(date_time.timetuple())

def human_time(unix_time):
    return datetime.datetime.fromtimesstamp(unix_time)

Pull data from CoinGecko¶

In [3]:
cg = CoinGeckoAPI()
bitcoin_data = cg.get_coin_market_chart_by_id(
    id="bitcoin",
    vs_currency = "usd",
    days = 30
    )
In [4]:
print(f'keys {bitcoin_data.keys()}')

keys dict_keys(['prices', 'market_caps', 'total_volumes'])

Transforming data¶

First we select only the first key (prices) wich contains a TimeStamp (number of seconds since) and the respective price. We transform the data into a dataframe so we can manipulate more easily.

In [5]:
bitcoin_data_price = bitcoin_data['prices']
data = pd.DataFrame(bitcoin_data_price, columns=['TimeStamp','Price'])
data
Out[5]:
TimeStamp Price
0 1695574805796 26570.733979
1 1695578469402 26657.753583
2 1695582049432 26600.275122
3 1695585670396 26447.459302
4 1695589238846 26505.534806
... ... ...
717 1698152477689 34606.406639
718 1698156033985 34271.579650
719 1698159608858 34443.238871
720 1698163262722 33689.133899
721 1698166609000 34074.789662

722 rows × 2 columns

  • we need to convert the TimeStamp column so can be a readeble time
In [6]:
data['Date'] = pd.to_datetime(data['TimeStamp'], unit='ms')
data
Out[6]:
TimeStamp Price Date
0 1695574805796 26570.733979 2023-09-24 17:00:05.796
1 1695578469402 26657.753583 2023-09-24 18:01:09.402
2 1695582049432 26600.275122 2023-09-24 19:00:49.432
3 1695585670396 26447.459302 2023-09-24 20:01:10.396
4 1695589238846 26505.534806 2023-09-24 21:00:38.846
... ... ... ...
717 1698152477689 34606.406639 2023-10-24 13:01:17.689
718 1698156033985 34271.579650 2023-10-24 14:00:33.985
719 1698159608858 34443.238871 2023-10-24 15:00:08.858
720 1698163262722 33689.133899 2023-10-24 16:01:02.722
721 1698166609000 34074.789662 2023-10-24 16:56:49.000

722 rows × 3 columns

Creating a Candlestick chart using Plotly¶

we gonna need to group the data by the min, max, first and last price for each date

In [7]:
candlestick_data = data.groupby(data.Date.dt.date).agg({'Price':['min','max', 'first','last']})
candlestick_data.head()
Out[7]:
Price
min max first last
Date
2023-09-24 26447.459302 26657.753583 26570.733979 26503.235603
2023-09-25 26059.043075 26357.550153 26257.583770 26287.076367
2023-09-26 26111.574712 26347.306498 26296.922317 26151.495532
2023-09-27 26166.382102 26805.628892 26204.757591 26288.209341
2023-09-28 26344.993094 27146.515346 26350.146895 27013.058110
  • importing and using the plotpy lib
In [8]:
import plotly.graph_objects as go

fig = go.Figure(data=[go.Candlestick(x = candlestick_data.index,
                                    open=candlestick_data['Price']['first'],
                                    high = candlestick_data['Price']['max'],
                                    low = candlestick_data['Price']['min'],
                                    close = candlestick_data['Price']['last'])
                     ])


fig.update_layout(xaxis_rangeslider_visible = False, xaxis_title = 'Date',
                 yaxis_title='Price (USD $)', title = 'Bitcoin Candlestick Chart Over Past 30 Days')

fig.show()